NDLI: Oblique Decision Trees using embedded Support Vector Machines in classifier ensembles

Content Provider	IEEE Xplore Digital Library
Author	Menkovski, V. Christou, I.T. Efremidis, S.
Copyright Year	2008
Description	Author affiliation: Athens Inf. Technol., Peania (Menkovski, V.; Christou, I.T.) \|\| INTRACOM S.A. Telecom Solutions, Peania (Efremidis, S.)
Abstract	Classifier ensembles have emerged in recent years as a promising research area for boosting pattern recognition systems' performance. We present a new base classifier that utilizes oblique decision tree technology based on support vector machines for the construction of oblique (non-axis parallel) tests on the nodes of the decision tree inducted. We describe a number of heuristic techniques for enhancing the tree construction process by better estimation of the gain obtained by an oblique split at any tree node. We then show how embedding the new classifier in an ensemble of classifiers using the classical Hedge(beta) algorithm boosts performance of the system. Testing 10-fold cross validation on UCI machine learning repository data sets shows that the new hybrid classifiers outperforms on average by more than 2.1% both the WEKA implementation of C4.5 (J48) and the SMO implementation of SVM in WEKA. The application of the particular ensemble algorithm is an excellent fit for online-learning applications where one seeks to improve performance of self-healing dependable computing systems based on reconfiguration by gradually and adaptively learning what constitutes good system configurations.
Starting Page	1
Ending Page	6
File Size	326670
Page Count	6
File Format	PDF
ISBN	9781424429141
DOI	10.1109/UKRICIS.2008.4798937
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-09
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Entropy Information technology Support vector machines Supervised Learning Working environment noise Support vector machine classification Machine learning Classification On-line Learning Decision Trees Decision trees Classification tree analysis Testing
Content Type	Text
Resource Type	Article

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